This August 16, 2011 webinar, hosted by DBTA with InfiniteGraph, examines the technology behind InfiniteGraph and explores common use cases involving very large scale graph processing, and social network analysis. InfiniteGraph was designed specifically to traverse complex relationships in big data, and provide the framework for products built to provide real-time network analysis, business decision support and relationship analytics. Moderator: Tom Wilson, President, DBTA and Unisphere Research. Presenters: Darren Wood, Chief Architect, InfiniteGraph, and Mark Maagdenberg, Senior Field Engineer, InfiniteGraph.
Social Networks – Facebook, LInkedIn, Twitter – connecting people to people or companies. most connected participants Influencers Important sub-networks Gaming – connecting players with other players; looking for central players SocialCRM – connecting companies to customers, cases, email HCM – connecting employees to projects, skills GIS/Geo-Spacial – connecting people to places/events (POI) (e.g. what’s around me?) Recommendation Engines – connecting people to places based on credibility of others recommending said places; FOAF, You might also like Computer/Phone/Utility Networks – connecting computer systems and networking components quickly detect issues/remediate problems. B2B or B2C - connecting areas to find shortest/cheapest routes on air, land, sea. Fraud/Crime Detection – connecting people to events, financial tx, phone conversations Recognize attack/threat patterns Web – connecting URLs, triple stores (RDF) Marketing – connecting people to web sites, habits. Intelligence – looking for bad guys by connecting phone calls between people, events. Transportation – calculating shortest routes by air, land, sea.
Some SNA questions: How highly connected is an entity within a network? What is an entity's overall importance in a network? How central is an entity within a network? How does information flow within a network? Degree centrality Bob has the highest degree centrality, which means that he is quite active in the network. However, he is not necessarily the most powerful person because he is only directly connected within one degree to people in his clique—she has to go through Sam to get to other cliques. Betweeness Centrality Sam has the highest betweenness because he is between Bob and Joe, who are between other entities. Bob and Joe have a slightly lower betweenness because they are essentially only between their own cliques. Therefore, although Bob has a higher degree centrality, Sam has more importance in the network in certain respects. Closeness As with the betweenness example, Sam has the highest closeness centrality because he can reach more entities through shorter paths. As such Bob’s placement allows him to connect to entities in his own clique, and to entities that span cliques Eigenvalue Bob and Sam are closer to other highly close entities in the network. Julie and Kate are also highly close, but to a lesser value.
Recognize common patterns of activity Complex chains of interaction